God I wish they'd back up all of those claims by offering a subscription of Kimi K3 and GLM 5.3, not some outdated GLM 4.7 instance that they then proceed to call a preview model and say that they'll remove it, leaving users only with GPT-OSS 120B which is nigh useless nowadays: <a href="https://support.cerebras.net/articles/9996007307-cerebras-code-faq" rel="nofollow">https://support.cerebras.net/articles/9996007307-cerebras-co...</a> and <a href="https://www.cerebras.ai/pricing" rel="nofollow">https://www.cerebras.ai/pricing</a><p>Guess they don't care about regular devs atm and are focused only on hardware sales.
Why would they offer a coding subscription and start competing with some of their biggest customers; when they are capacity-bound and companies like OpenAI will take however many wafers that Cerebras sells to them?<p>OpenAI's Sol ultrafast (powered by Cerebras) is still in preview, presumably because they're overall capacity bound.
> Why would they offer a coding subscription and start competing with some of their biggest customers; when they are capacity-bound and companies like OpenAI will take however many wafers that Cerebras sells to them?<p>Because they already have / had an okay coding subscription product for a bit and it gives them visibility and mindshare (in regards to their hardware, even if they don't compete with other providers that much). They could do what Kimi did - make a good subscription with good models, once you get enough customers to get some good PR and such, pause the signups so you don't have to spend more on running the service than you want/can. Do enough of that and people will talk about your offerings organically, make yourselves known to even devs as "That one company with their own hardware and the super fast subscription." experiencing which would do more than any marketing.
Coding subs are good when they promote usage and adoption of <i>your</i> models in enterprises at API rates.<p>Cerebras is a B2B hardware company. It feels like a distraction: think of the opportunity cost, and resources/headcount not working on other things that would drive more impact.<p>Should NVIDIA do a coding subscription too? I'm sure they can make money off it, but I think it would be -EV.
They do have Cerebras Code<p><a href="https://www.cerebras.ai/code" rel="nofollow">https://www.cerebras.ai/code</a><p>But it's not fully open to just anyone, I wasted time signing up to find out that I couldn't even sign up for it to test it out.
>Guess they don't care about regular devs atm and are focused only on hardware sales.<p>Why would they want to target regular devs right now? If they sold to regular devs instead of enterprises, the complaint wouldn't be about model choice, it'd be about how expensive it.
I don’t think they will until they change the architecture.<p>They don’t have a prefix cache like other providers, or at least don’t have a discount in their billing structure. Each message charges for the whole context window. It’s wildly more expensive for long multi turn scenarios with lots of tool calls (coding). It’s better for short few turn tasks.<p>Edit: I don’t know if they actually have a proper cache. This could just be a billing artifact.
GLM 4.7 is gone (at least for us), with no suitable replacement from Cerebras. I think all they care about now is hardware and OpenAI hosting.
> GPT-OSS 120B which is nigh useless nowadays:<p>I still think that was a really great model that got overlooked. It was really great in terms of latency/throughput while still being fairly intelligent.<p>I was planning on using it for a design tool, but moved over to luna since it's comparable speeds and cost for a lot more intelligence.
> I was planning on using it for a design tool, but moved over to luna since it's comparable speeds and cost for a lot more intelligence.<p>Everyone should occasionally go back to the old models to see how much worse they were, like even a year ago you could generate results but they were typically full of bugs and you have to fix a non-insignificant amount of it all manually: <a href="https://blog.kronis.dev/blog/i-blew-through-24-million-tokens-in-a-day/" rel="nofollow">https://blog.kronis.dev/blog/i-blew-through-24-million-token...</a><p>Admittedly that post was before agentic development truly took off and that 3k EUR figure when paying per API tokens would nowadays be closer to like 6k EUR for the volume of work I do, but still.<p>It's the same how Qwen 2.5 was pretty problematic for anything remotely serious, same with Qwen 3 Coder Next (80B), and at least the most recent versions are getting better but still not <i>quite</i> good enough in real world use cases outside of benchmarks. They've come a long way, regardless!
>Everyone should occasionally go back to the old models to see how much worse they were, like even a year ago you could generate results but they were typically full of bugs<p>Oh yeah, I'm still amazed how good the current iteration of models are for coding (I have a fear it's too good to be true - so will get taken away..). Exactly a year ago I switched from GPT 5 to Gemini just because the coding with R language was terrible; and even with Python it kept forgetting and mixing basic stuff. Gemini at the time had much longer context window and was miles ahead on R syntax.<p>Current experience of just leaving a Codex Agent chug until a stable solution is completed is still mind blowing to me.
As MoE with 5B active parameters it's pretty fast. But you still need a lot of vRAM, or have to run small quantitations. Qwen models just gave you more bang for your buck, and the gap became worse with every qwen release
gpt-oss-120b is absolutely unusable over Cerebras. It fails to call tools half the time and just continues to think about what tool it'll call repeatedly. Like it says it'll call a tool and then it doesn't, and then it says it'll call the tool again and then it doesn't, and it just does that in a loop forever. It's awful. Also forgets to end the thinking block too. Even if the model itself was just-okay for its time, even at 1000t/s+ it's not worth it. And it's EXPENSIVE, like $5 per minute expensive
Why they should go with Chinese models if they have a line up of gpt models and a very good partnership with someone who lives in the same jurisdiction and not in the country that convinces their citizen that it’s a good idea to go on war with western world ? Just curious ?
> Why they should go with Chinese models<p>Because they generated some buzz and are near-SOTA and would be a great benchmark for a PoC subscription that doesn't necessarily aim to compete with other vendors at a similar scale (since their main business is the hardware). Mistral is conceptually cool but is lagging behind. I guess Muse Spark and Laguna would also be okay, just not as recognizable. Meanwhile both Kimi K3 and GLM 5.3 are near-SOTA in performance and considerable in size, a great choice for proving the platform!<p>As for the 2nd part of your question - that wasn't a relevant concern or consideration here, unless the models would be tainted to a degree to prevent them from having a good coding subscription that gets more developer mindshare towards what their chips can achieve and generate some good PR.
Cerebras is the fastest provider <i>by far</i> on OpenRouter, and gpt-oss-120b is still very useful. They have backed up their claims very well.<p>> Guess they don't care about regular devs atm and are focused only on hardware sales<p>They aren't trying to make a few bucks off tokenmaxxers. They're trying to be the underpinning of compute for all AI. They're going to beat Nvidia.
I was hoping to see Cerebras launch something other than GPT-OSS-120b in production this week, especially with GLM4.7 going away.<p>If they could launch Qwen 27b or Deepseek Flash that would be amazing.
I think the fun takeaway from this is that GPT 5.4 is probably 45B active parameters and GPT 5.6 Sol is closer to 50B.
You cannot infer this because they only show the tokens per second per user. One way to get a higher number is to have fewer users per chip.<p>I'm pretty sure Cerebras has a confidentiality agreement with OpenAI, and this press release was carefully constructed to avoid leaking details about the model weights. For example, the graph of tokens per second vs. tokens per second per user doesn't have any numbers that would allow you to translate between the two. (And in any case the relationship depends on the model.)
They show that CS-4 can't really do batching (or rather it can't properly benefit from it), total throughput barely changes (25%?):
<a href="https://cdn.sanity.io/images/e4qjo92p/production/6a132331880d41c8ea20b584f4fd37c270741692-1920x1080.png" rel="nofollow">https://cdn.sanity.io/images/e4qjo92p/production/6a132331880...</a><p>Which I think makes it feasible to approximate activation from CS-4 tokens per second per user.
(Where did you see that?)<p>This was also interesting: "CS-4 delivers more than 1,000 tokens per second on models exceeding 10 trillion parameters." Was it known that there were 10 trillion parameter models in use?<p>I think the frontier providers keep the size of their models carefully hidden.
You don't really need to train a 10T model to test cerebras against a 10T model. You can feed it an untrained (randomly initialized) model and benchmark it. Result will be gibberish but performance the same.
Mythos/Fable are around 10T:<p>> According to FT, industry estimates say Anthropic's most advanced Mythos 5 has about 8 trillion parameters and Fable 5 about 5 trillion<p><a href="https://www.reuters.com/technology/bytedance-targets-mega-ai-model-nearing-anthropics-mythos-ft-reports-2026-08-07/" rel="nofollow">https://www.reuters.com/technology/bytedance-targets-mega-ai...</a><p>I believe this report has confused Opus (which is known to be around 5T) and Fable.<p>Other reports say 10T. See for example <a href="https://eu.36kr.com/en/p/3760679047267075?ref=explainx" rel="nofollow">https://eu.36kr.com/en/p/3760679047267075?ref=explainx</a> where Musk talks about the models being trained on Colossus2
I'm confused. I thought Mythos 5 and Fable 5 were exactly the same model just with a different security layer in front of it.
Could they mean the Mythos 5 Preview?
> I believe this report has confused Opus (which is known to be around 5T) and Fable.<p>5T for Opus feels quite high though. DeepSeek V4 Pro is a mere 1.6T and often described as a match with Opus in overall quality. Even the largest open models in common use are around 2.8T.
> and often described as a match with Opus in overall quality<p>It's not. Idk about who has more T's but, unfortunately, DS4 pro is not a match to Opus, at least not Opus 4.8.
It's not an Opus match.<p>The difference is very visible in long tail applications. Exactly where you'd expect parameter count to matter.
pretty sure 10 trillion parameters is now the norm among closed ai labs, given that nvidia also references the same 10 trillion number for their nvl72 racks
It's rumored fable is around that 10T number
If this is true, it's even more impressive that some of the open weight models that are <3.5T in size, approx 33% of its size, are within a few points of it in the artificial analysis leaderboard.
Not necessarily, there could be diminishing returns on mere parameters count .<p>There is nothing to say for example a 1 Quadrillion parameter model will be vastly more intelligent than current SOTA especially since new training data is largely synthetic today
GLM 5.3 is "only" 753B parameters. Much much smaller.
You want to take a look at the "Scaling Laws" paper, so you can extrapolate from these numbers.
And GLM is only 0.7T!<p>But these labs distill off the larger models. Both officially at the labs with the big ones, and unofficially. We need the giant models to get the smaller models.
Fable is most definitely nowhere near 10T.<p>The cost to train and infer that would be insane, even by today's standards.
Fable is strongly believed to be around 10T. The most conservative estimate I've seen is 8T.<p>Eg: <a href="https://www.reuters.com/technology/bytedance-targets-mega-ai-model-nearing-anthropics-mythos-ft-reports-2026-08-07/" rel="nofollow">https://www.reuters.com/technology/bytedance-targets-mega-ai...</a><p>That reports Mythos as 8T and Fable as 5T, but I think they mean Opus as 5T, which is widely known, eg: <a href="https://eu.36kr.com/en/p/3760679047267075?ref=explainx" rel="nofollow">https://eu.36kr.com/en/p/3760679047267075?ref=explainx</a><p>Both Grok and Bytedance are training 10T models.
The fact that Musk claims Opus is 5T to justify why Grok is far behind should be taken with a massive grain of salt given he's a recidivist mythomaniac.<p>Honestly if Opus is 5T parameters while being matched by the biggest open models that are at least twice smaller, it would mean that the US is already behind China in the AI race, despite a significant edge in compute.
The open models don't really match Opus.<p>For example I regularly do Fable+Opus agentic coding runs over 24 hours without intervention.<p>I think I've had GLM do a run that was a few hours. That's the closest I've had an open model come on that kind of work.
Yes. And Opus goes a very long way compared to Fable, Anthropic isn't doing any favour, it's clearly just 2 models with a very different amount of parameters.
If Fable is seriously around 10T and Kimi K3 sidles up to it at 2.4T<p>That would be extremely surprising and a massive blunder by Anthropic in model design architecture ... which I highly doubt to be the case.
Wasn't Opus ~1.5T and Fable is about twice that?
Kimi K3 is a 2.8T model that's available at about 1/4-1/3 the cost of Fable from multiple providers on openrouter. The math doesn't seem wildly off.
> The cost to train and infer that would be insane, even by today's standards.<p>This assumption is likely what has led to the erroneous failure.<p>Enterprise compute per rack has scaled multiple fold in the last 3-5 years. Alongside the training efficiency gains & datacenter scale increases, even 50T+ is well within reach at the top end.
Yeah, that's insane, you'd need to have many billions of dollars and buy up a huge chunk of the worlds memory supply to do that /s
Didn't they say that they can support bigger models now?
Memory capacity on the WSE is the same as before, but access to off-wafer memory is much slower, so the sweet spot is a given fixed balance of memory and compute. They have announced a partnership with AMD in which CPU/GPU hardware is used for part of the workload and the WSE-3 machines are used for inference for specialized smaller models, but I'm not really sure of the details on that.<p>And there is, of course, the educated guesses about what WSE-4 will be, one being adding a LOT of stacked SRAM or DRAM to tip the balance towards memory (which could also be done by having a few different tile designs with various configurations of compute and memory capacity). I am curious about which way they'll go.
AMD along with cerebras may probably compete with NVIDIA monopoly in near future. Also, NVIDIA will have competition form multiple companies. Just my prediction.
Maybe, but GPU is just one aspect of NVIDIA's dominance. If you are buying Vera Rubin GPUs, you're getting an NVL72 rack, which is only one of several racks that you're probably buying. You'll also need your NVIDIA racks with NVIDIA networking & storage gear, too. At the end of the day, they're "vertically integrated" for your accelerated computing data center (e.g. the "AI Factory"). This doesn't even count the software layer, where CUDA + CUDA-X (not to mention the software for all the sysadmin pieces) has a huge first mover advantage over anyone else.
Like NVIDIA bought Groq, AMD might do well buying Cerebras.
AMD did enter into an agreement to buy Taalas, which is speculated [1] will be used to augment their Helios offering.<p>1. <a href="https://www.youtube.com/watch?v=3MKRjt59hh4&pp=0gcJCRMMAYcqIYzv" rel="nofollow">https://www.youtube.com/watch?v=3MKRjt59hh4&pp=0gcJCRMMAYcqI...</a>
They are Ex AMD employees. Nvidia tried back, but they rejected.
Press doubt. Single GPU? Maybe. MultiGPU behemoths like NVL144 and NVL576? I don't think so.<p>NVLink is at gen9. they had a lot of teething problems and can codesign the hardware and software.<p>in the name of openness (AMD's only """weapon"""), the UALink spec is a hodgepodge of corporate opinions with very different implementations (looking at you, Broadcom). at spec version 1 (in hardware).<p>I wish them good luck as I really like AMD, but they compete no more on this than Lambo vs Bugatti.
It's not really a far fetched prediction: high margins and huge market attract competition, that's just the law of economics.
Cerebras is very fast but you can basically never use it because of its scarcity
> CS-4 delivers more than 1,000 tokens per second on models exceeding 10 trillion parameters<p>Oops did they just out GPT-5.6 sol’s parameter count?
Sol is supposed to be 5T according to rumour. The imminent Astra is allegedly 10
I mean we kinda know the frontier models are multi trillion parameter models. The only open weights that are close to the frontier are that size too
cerebras model are different size then the original models
Just a reminder for everyone that we are only several years and 3 or 4 iterations into hardware being optimized for LLMs. We should all expect orders of magnitude improvement in speed and/or cost over the next 5 years. Then we can have fun conversations about "unlimited" "intelligence" and about what the price wars and profit margins of consumer AI products are when your average ChatGPT user costs the company $0.10 per month.<p>> CS-4 delivers more than 1,000 tokens per second on models exceeding 10 trillion parameters<p>Wow!
And, the software side isn't finished being optimized, either. We've seen with Qwen 3.8 27B and DeepSeek V4 Flash 0731 and GLM 5.3 that quite small models can pack a punch. Intelligence density will improve, efficiency of kernels will improve, efficiency of KV caching and MTP will improve, algorithms for splitting workloads across compute units will improve.<p>It'll all be as cheap as DeepSeek was before the price hike. And, it'll become more and more realistic to run near-frontier intelligence on personal devices.
> Then we can have fun conversations about "unlimited" "intelligence" and about what the price wars and profit margins of consumer AI products are when your average ChatGPT user costs the company $0.10 per month.<p>We can have that discussion now: sounds like that would kill OpenAI and Anthropic
What LLM-specific hardware improvements should one expect? Seems to me that LLM inference is simple architecturally (matmul et al) so most scaling in hardware should come from general improvements (memory BW, packaging, interconnect, power).
What you describe is basically Cerebras case, at the bottom it's just a really big die (about x28 an NVIDIA GB200) with a lot of work to reduce memory latency and improve throughput.
What it's actually amazing is how can they make a chip so big and still have a decent yield to be commercially viable.
Hence why taalas was one of the best strategic acquisitions of the year.<p>I'm honestly baffled they were not acquired by somebody else (sorry AMD).
Taalas will be one of the great disaster investments of the early AI era. It'll be a near total write-down.<p>The absolute worst market time to etch a model to a chip is right now (very rapid iteration). There is no scenario where they can keep up. The Taalas approach will be viewed as comically foolish within just a few years.<p>Cerebras will win in terms of approach.<p>It's 1998: hey, I can drastically speed up your web service, let's etch it right to silicon.
I would still pay ~500 for a chip that runs 10kt/s of a ~100b model on my machine even if the half life is 6months. I m sure my employer would too.<p>Qwwen3.5 122b was released 6 months ago and is still best in class overall 100-140 B param model.
> I would still pay ~500 for a chip that runs 10kt/s of a ~100b model on my machine even if the half life is 6months.<p>....<p>:T<p>Considering the 8B model uses 53 billion transistors, that's 6.625 transistors per parameter.<p><a href="https://taalas.com/products/" rel="nofollow">https://taalas.com/products/</a><p>Assuming they can get it down to 3 (somehow), that's still 300 transistors, or 5.565 RX 9070s.<p><a href="https://www.techpowerup.com/gpu-specs/radeon-rx-9070.c4250" rel="nofollow">https://www.techpowerup.com/gpu-specs/radeon-rx-9070.c4250</a><p>You're looking at<p>1) waiting for another 3-5 generations of transistor improvements before it can fit into a single conventional chip, or<p>2) another generation before getting a monster of a chip (1000+ mm^2), and prices for flawless etching scale quadraticly (likely $1000+ for manufacturing costs alone).<p>Could happen, but it's a long shot for a market that could be satiated by specialized accelerators.
500 what? You’re missing the unit
10000%
> It's 1998: hey, I can drastically speed up your web service, let's etch it right to silicon.<p>I distinctly remember 32-bit/33 MHz PCI accelerator cards for SSL being a real thing (for use on OpenBSD or FreeBSD), in an era when something like a single core 700 MHz Pentium 3 1U system was a relatively powerful individual bare metal httpd box.<p><a href="http://www.aster.si/partnerji/compaq/atalla/axl200.html" rel="nofollow">http://www.aster.si/partnerji/compaq/atalla/axl200.html</a><p>The CPU load of doing a lot of SSL purely in software was a problem in terms of scaling things up, so this was one attempt at a (very short lived) solution. Note that this predated TLS1.0.
> The absolute worst market time to etch a model to a chip is right now<p>Slightly disagree. It really depends on the price-point at which they can do that etching. ~1k usd / ~30B model in a hdd-sized case that fits on your desk? I'd buy one right now, even knowing that I'm "stuck" with whatever model of the day is.
The 500x efficiency gain makes their approach a no brainer. Just make a new chip every 6 months, you still win.
maybe AMD wants the IP to deploy it once ai model development slows down in a few years. Or, their large cloud customers do want to burn through silicon, basically paying rent to AMD for models etched on silicon.
I just want to but hardware so I can run a model at home that is fast. I don't see myself installing a server that burns almost two hundred kilowatts but maybe a card which runs a 27B Qwen...
It's 2026: let's etch nginx into silicon and get 10,000,000 rps at a cost of 0.1 US/day.<p>Yes, please!
Etched model into a chip? A… mobile chip eventually? Seems prescient.
This is part of why I think the data center build-out is a bubble. We've barely scratched the surface when it comes to hardware optimization. We'll see exponential improvements in energy efficiency and speed over the next decade. Exponential, not linear.<p>GPUs really aren't that great for AI. They just happen to be the best chips we have in mass production right now for this work load, and it takes time to field new designs. Basically every chip engineer on the planet is working on this right now.
Whether it's a bubble or not depends on how much the demand for compute and the type of workload keeps growing, though.<p>If AI tends to be something used mainly in ideation and development, which is how a lot of people use it today, then once consumer hardware gets good enough you could see a bunch of the current data centre workloads move onto consumer devices.<p>But if AI starts being used more in repeatable, operational workloads I think it makes sense to have significant cloud infrastructure for it. TBH I haven't seen much of this, and I've been skeptical about people using agents for much of anything when it can be done with just software. But we are starting to see more of this kind of workload, like the taggable Claude in your slack etc that people seem to really love.
By the way, this is the same argument that Michael Burry used to short Nvidia.<p>He claims that GPU depreciation/obsoletion is much faster than hyperscalers are assuming because new chips will be much better. He's being proved wrong right now because H200 rental prices have been claiming for the last 8 month despite B200 having 10-20x better inference efficiency.[0]<p>The logic is fundamentally flawed in my opinion. Let's use future Nvidia chips being much better optimized for LLMs for example.<p>New Nvidia chips 10x better than H200 --> data centers buy a lot --> Nvidia profits a lot.<p>New Nvidia chips 10x better than H200 --> data centers don't buy --> no faster than expected obsoletion.<p>In other words, the very act of buying many new Nvidia GPUs would be the event that causes faster than expected obsoletion. Yet, if you don't buy those new Nvidia GPUs, then there is no faster than expected obsoletion.<p>We also live in a world where there is competition. If Amazon doesn't buy but Microsoft does, suddenly Microsoft can offer better $/token prices.<p>[0]<a href="https://inferencex.semianalysis.com/inference" rel="nofollow">https://inferencex.semianalysis.com/inference</a>
1. The same isn’t necessarily true of the rest of the hardware stack which may be reused between accelerator generations.<p>2. You’re missing the “New Nvidia chips 10x B200, compute requirement grows less than 10*software improvements YoY -> buy less Nvidia.” Valuations are based on forward projections (>1T annual for NVDA) which can be revised down leading to a drop in valuation.<p>> If Amazon doesn't buy but Microsoft does<p>The big 3 all have their own proprietary accelerators. Meta is buying TPUs as well for now.<p>I would bet Nvidia’s major customers in 2 years are neoclouds and it seems that Jensen is making the same bet.
1. So this makes Burry’s argument even less convincing since those auxiliary hardware can last longer.<p>2. Jevons Paradox. More efficiency should lead to bigger models, faster inference, and more total tokens.<p>3. By all accounts, Trainium and Maia and Meta’s internal chip are struggling to keep up with Nvidia. That’s why they order as many Nvidia chips as possible. They’re not giving up but it isn’t as easy as buying stock Arm cores and taking them to TSMC.<p>Neoclouds may very well be Nvidia’s biggest customers and this probably what Nvidia wants.
1. Not really, current valuations are priced for persistent 80%+ margins based on spot. If auxiliary hardware lasts longer (I.e. next gen GPU reusing the same shell) then that reduces supply pressure and spot prices.<p>2. Jevon’s paradox is about total consumption, not margins. Valuations are about margins (and their projections). Many coal mine owners went bust despite increased total coal consumption.<p>3. Source? Gemini for example is 70% on TPU. I have yet to see data on Maia-300 beyond Microsoft PR. Remember it doesn’t have to be <i>better</i> it has to be more cost efficient. The overwhelming majority of inference spend does not care if token output is 20% slower if it is 50% cheaper.<p>> Neoclouds may very well be Nvidia’s biggest customers and this probably what Nvidia wants.<p>What Nvidia <i>needs</i>. Whether neoclouds can stay competitive vs hyperscalers paying Nvidia tax is far from clear, particularly when inference margins compress.
I wonder: in world where inference is cheap, how many engineering agents that use simulation as their feedback we will use?<p>In the scenario, engineering everything becomes so easy - so why not optimize everything? every component, every product, every system?<p>And maybe llm's could invent. So even more to simulate. And simulation is inherently compute-heavy.<p>So unless there are some other bottlenecks, we'll use a lot of simulation servers.
On the plus side, lots of cheap servers to swoop up :)
But power hungry.<p>In that 5+ year timeline, the compute per watt could change by three orders of magnitude.<p>GPUs are to LLMs what CPUs are to gaming — not a good fit.
A cursory estimate courtesy of ChatGPT suggests that there is a grand total of one order of magnitude or less of power efficiency improvement available compared to current Blackwell if the entire system’s power consumption outside the ALUs went all the way to zero.<p>If you want three orders of magnitude improvement, you probably need to find two of those orders of magnitude somewhere else: process improvements, different ALU design, model architecture changes, etc.
Look at their power supply, it’s not something you can run in a home lab. Unfortunately most of that will likely go to the bin eventually :(
True, I have to agree with you. The AI giants might be investing a huge amount of money in generation 1 technology. There might be a much better way to do it just around the corner. They might know this and thus the hurry to IPO.<p>A rough analogy would be if the first generation of ISP's spent billions on dial-up exchanges, when fibre could be invented next year.
Exactly right, and nVidia is protecting their moat through business practices rather than genuine product innovation.
By the time these gigawatt datacenters are done being built the hardware will be so far behind state of the art they may be mostly useless.
Congratulations! You have just realized that the AI data center build out is a total scam, built on both the insurmountable trillions of debt, and the assumption that <i>only</i> GPUs are all we need to continue scaling.<p>There exist other AI accelerators (TPUs, ASICs) that perfectly exceed the throughput that LLMs need to scale as well. But the true solution is more software optimizations. There's a tiny handful of them but more needs to be discovered so that we can reduce building hundreds of more data centers as the alternatives mature.<p>As better software becomes more useful for the alternative AI hardware for developers with LLMs running efficiently you then would have more choices of hardware to run your LLMs on rather than just only GPUs.
TPUs and ASICs run in data centers too. Your argument only holds true if there's some satisfied limit to demand for inference. If not, data centers will continue to spring up to host more and more agents. Even if agents were running on hardware and software as efficient as the human brain, its conceivable we want trillions of them running at any given time which would require data center scale.
I wonder what this looks like in 5 years... Will there be a massive push to repurpose these giant boxes into housing? Will they get turned back into the farm land from where they came? When a data center goes bust, what happens to the parts left behind?
I'd think the infrastructure would tend towards factories, smelters, and so on. Industrial things that have reasonably high power demands, can use the building, and don't care about the lack of windows.<p>They're typically not built where you want housing, and the buildings are distinctly the wrong shape.<p>If you can't use the power infrastructure profitably my next thought would be warehousing.<p>But also... we've seen a pretty continually increasing demand for compute. Even if AI busts a bit (or becomes a bit more efficient) I bet most data centres stay data centres, just less profitable ones.
Huh, why I'm not surprised that HN is full of opinions confidently stated without any numbers or resources to back up?<p>> built on both the insurmountable trillions of debt, and the assumption that only GPUs are all we need to continue scaling.<p>Insurmountable according to whom? And who assume that only GPUs are all we need to continue scaling? Google, Amazon, Microsoft, Meta and OpenAI, all have or plan custom non-GPU AI chips. Do they plan to use them not for scaling?
Interestingly they’re still on the WSE-3 (5nm TSMC) wafer chip and slightly bumped up the specs there (overlocking mostly it seems), for why it’s called WSE-3 Turbo now. I think people were also expecting WSE-4, as it’s been 2 years now since WSE-3 was launched.
If cerebars is performing well, why didn't its predecessor, server S-3, become the largest API token provider on OpenRouter, surpassing the official model releases?
Without having any inside information, one possible theory:<p>All or a vast majority of of the cerebras manufacturing capacity was going to a few companies that aren't publicly available inference providers on openrouter, for their own internal use.<p>or<p>The asking price of the S-3, no matter how speedy it might be, for small/medium size customers made it economically prohibitive to purchase and use to sell public inference vs. buying more common nvidia b200 or whatever.
Cerebras provides high-speed inference at high cost. It's never going to be the cheapest and thus it will probably remain niche.
But that's supply and demand, not technology. Right now a lot more people want their inference than they can supply. as supply catches up in the next 5-10 years, the underlying tech at scale is probably cheaper than GPUs per token produced.
Probably the same reason why there are more people who takes buses, subways, trains than drive Ferraris.
If you're willing to pay a significant premium for latency, why use openrouter? And anyway Cerebras only supported a few specific models.
Cerebras capacity was pretty much entirely bought out at some point. We needed it and couldn't get it.
The WSE is very expensive to build, and they have a waiting list of customers who are already willing to pay a lot of money for the available supply.
it only takes ~445 GB300 NVL72 (about $22b) to run ALL of openrouter demand for a year. Microsoft rolled out $32b of DC 2026Q1.<p>imo the issue is that most openrouter demand is inauthentic activity (things that anthropic and openai models will refuse to do like pretend to not be bots when interacting with humans)
I thought your numbers must be wrong.<p>So I plugged 288 trillion tokens/month (OpenRouter's current rate), 500 billion MoE model average, and the math comes out to be around 620 B200 GPUs minimum.<p>So basically, OpenRouter's volume must be absolutely tiny compared to the volume hyperscalers are getting.
It is worth mentioning, the OpenRouter demand isn't static though. It has increased week on week since early 2026.
I was curious so I looked it up: looks like a GB300 NVL72 is about $4M. So $22B would buy you 5500 such racks, no?
Because they aren't selling inference, they're selling hardware. The only reason they sell any tokens on OpenRouter is so they get on the benchmark that shows them as the fastest provider. It's free advertising.
I use Cerebras via OpenRouter. It’s every bit as fast and reliable for my needs as claimed. I suspect the reason is that they can either be making peanuts selling inference to plebs like me via OpenRouter, or making bank selling the more expensive models to businesses directly. In short: I would be very surprised if they have die capacity, and are at this point maximising revenue per chip.
Impressive that this is an "interim" product, the start of a new line that ought to be continued with the WSE-4 family, where they are supposed to use a 3nm process and, maybe, 3D stacked SRAM. The modular architecture also points towards field upgrades that are badly needed for AI datacenter builders.
Conspicuously missing: power consumption figures
Really wish they’d host more models for us normies. My guess is OAI will buy / subsidize them with terms that will close off open models.
It would be even better if a version available to individual users were released soon.
They do offer API services to individual users... though with a set of models that makes it unlikely that you want to use it. They are promising Qwen 3.8 27B any day now though*.<p>if you have the money as an "individual user" to purchase one of their racks... save your money and retire.<p>* Actually they sent out an email claiming they already have it, but I don't seem to have access, they're promising to release it to the "shared tier" any day now.
not only that, but I was so happy with their GLM 4.8 that they got rid of yesterday :(
> save your money and retire.<p>Now that this hypothetical person has retired, what are they gonna do all day? Just sit on the beach and drink Mai Tais? If that's what they wanna do, sure, but nerds gonna nerd, and if I had that kind of money to retire on, I'd totally buy some ridiculously expensive AI box for fun.
I’ll get that 250kW home power service dropped in next week!
needs an sla that says power will never ever ever go out or else you will have a useless shattered plate of silicon.
I'd like to see a consumer version too, I don't need a whole rack of them. I probably can't even afford one gpu-sized one
can these vibe coded sites please set a max width and overflow so their sites work fine on mobile
> Introducing the all new Cerebras CS-4, a revolutionary rack-scale solution that delivers upto 30x faster inference compared to GPUs, enhanced economics, and a simple path todeploy [sic] hyperscale capacity.<p>Did nobody proofread this?
If they had ask Claude it would probably look like this: Introducing the all new Cerebras CS-4, a revolutionary rack-scale solution that delivers up to 30x faster inference compared to GPUs, enhanced economics, and a simple path to load-bearing hyper scale capacity.
Sometimes I wonder if mistakes are now used to indicate the possibility that a human actually wrote it.
Well at lest it's written by a human.
Maybe it is just part of their "compact design".
An error no frontier LLM would make, eh
What's the sticker price? If I have 20 million in the bank can I just like buy one or what
KV caching status?<p>What's the point of 1000tok/s if you have to do prefill on every agentic turn which at 100k depth would make it 1.5 min latency every turn?
The comparison seems incomplete. CS‑4 is a full rack-scale system with three wafer-scale processors, but the exact GPU models, GPU count, power consumption, price information are not disclosed.
We still don't know if buying a multi-GPU rack (or racks) is cheaper and/or more efficient in power.
The fact that they didn't disclose these numbers makes me believe that the numbers are not in their favor. And personally, makes me see them as disingenuous.
> enabling massive clusters and models with more than 50 trillion parameters
only 44GB * 3 of VRAM per rack :O<p>I guess you'd need a DOZEN(s) of these to host a large model with long context KV caches?
That "GPU" comparison is the vaguest i seen so far
True, it's also "per user", somehow, but I think it's a misleading metric. Cerebras chips take the whole wafer?<p>A single TSMC wafer contains 60 to 65 B200s, assuming 70% yields that's 40ish wafers per die.<p>Cerebras cannot redefine wafer economics.
Cerebras should slowly also move to dgx/ryzen market for a desktop version for masses at affordable price yet providing substantial tokens/second on desktop
I wonder what are the benchmarks of hashcat on different hashes.
Five years from now, I don't know why anyone will still be using Nvidia for inference. Note that Cerebras is for inference only, not for training.<p>I understand that Cerebras has competition, but this bodes even more poorly for Nvidia for inference. Nvidia may still have a role to play for training, however.
NVIDIA has the best supply chain in the entire game. They are the only ones who can produce at their scale. You really shouldn’t underestimate their position
Nvidia is at this time a pretty well run company tech wise. They are going to keep iterating on the inferencing hardware stack over the next five years too.
Cerebras is only claiming ~2x the performance of Groqvidia which usually isn't enough for people to switch.
OpenAI needs to immediately move to acquire Cerebras.<p>Nvidia's extreme margin is the opportunity for OpenAI's cost reduction. Buying Cerebras would pay for itself and they should take all of its future production (after filling required contracts).<p>Right now China's models have no silicon moat. Cerebras as a drastic speed-up / cost-reduction potential, can assist in building a competitive moat. And every time a Cerebras pops up, OpenAI or Anthropic should eat them if at all possible.<p>There's no stand-alone frontier AI company of great scale in the near future that doesn't have a large silicon advantage in-house. Apple knew it in smartphones, Google figured it out a long time ago as well.
Is it just me or is it bizarre that they're advertising old open-weight models.<p>GLM 4.7 (December 2025) not 5 (Feb) 5.1 (April) or 5.2 (June). 5.3 (4 days ago) is, to be fair, not open weights yet... but there's a lot since 4.7.<p>Kimi K2.7 (April) not K2.7-code (June) or K3 (July).<p>Gemma 4 (April), Llama (April), and gpt-oss (August 2025) are up to date, but old (for models).<p>Meanwhile the closed source GPT 5.6 sol is up to date (June)...<p>Should potential purchasers take away from this that they're not going to be able to run recent models unless they front the cost of developing software or something?
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